Recognition of activities and objects plays a central role in surveillance and computer vision applications, see A. F. Bobick, “Movement, activity, and action: The role of knowledge in the perception of motion,” Royal Society Workshop on Knowledge-based Vision in Man and Machine, 1997; Aggarwal et al., “Human motion analysis: A review,” Computer Vision and Image Understanding, vol. 73, no. 3, pp. 428-440, 1999; and Nevatia et al., “Video-based event recognition: activity representation and probabilistic recognition methods,” Computer Vision and Image Understanding, vol. 96, no. 2, pp. 129-162, November 2004.
Recognition, in part, is a classification task. The main difficulty in event and object recognition is the large number of events and object classes. Therefore, systems should be able to make a decision based on complex classifications derived from a large number of simpler classifications tasks.
Consequently, many classifiers combine a number of weak classifiers to construct a strong classifier. The main purpose of combining classifiers is to pool the individual outputs of the weak classifiers as components of the strong classifier, the combined classifier being more accurate than each individual component classifier.
Prior art methods for combining classifiers include methods that apply sum, voting and product combination rules, see Ross et al., “Information fusion in biometrics,” Pattern Recognition Letters, vol. 24, no. 13, pp. 2115-2125, 2003; Pekalska et al., “A discussion on the classifier projection space for classifier combining,” 3rd International Workshop on Multiple Classifier Systems, Springer Verlag, pp. 137-148, 2002; Kittler et al., “Combining evidence in multimodal personal identity recognition systems,” Intl. Conference on Audio- and Video-Based Biometric Authentication, 1997; Tax et al., “Combining multiple classifiers by averaging or by multiplying?” Pattern Recognition, vol. 33, pp. 1475-1485, 2000; Bilmes et al., “Directed graphical models of classifier combination: Application to phone recognition,” Intl. Conference on Spoken Language Processing, 2000; and Ivanov, “Multi-modal human identification system,” Workshop on Applications of Computer Vision, 2004.